Extract Mark Indicators

Mark Indicators¶

Overview¶

Developer Note: if you may make a PR in the future, be sure to copy this notebook, and use the gitignore prefix temp to avoid future conflicts.

This is one notebook in a multi-part series on clusterless decoding in Spyglass

• To set up your Spyglass environment and database, see the Setup notebook
• For additional info on DataJoint syntax, including table definitions and inserts, see the Insert Data notebook
• Prior to running, please familiarize yourself with the spike sorting pipeline and generate input position data with either the Trodes or DLC notebooks (1, 2, 3).

The goal of this notebook is to populate the UnitMarksIndicator table, which depends on a series of tables in the spike sorting pipeline:

• SpikeSorting -> CuratedSpikeSorting -> UnitMarks -> UnitMarkIndicators

While clusterless decoding avoids actual spike sorting and curation, we need to pass through these tables to maintain (relative) pipeline simplicity. Pass-through tables keep spike sorting and clusterless mark extraction as similar as possible, by using shared steps. Here, "spike sorting" involves simple thresholding (sorter: clusterless_thresholder). The populate_mark_indicators will run each of these steps provided we have data in SpikeSortingSelection and IntervalPositionInfo.

SpikeSortingSelection depends on:

• SpikeSortingRecording
• SpikeSorterParameters
• ArtifactRemovedIntervalList.

SpikeSortingRecording depends on:

• SortGroup
• SortInterval
• SpikeSortingPreprocessingParameters
• LabTeam

Imports¶

In [2]:

import os
import datajoint as dj
from pprint import pprint

# change to the upper level folder to detect dj_local_conf.json
if os.path.basename(os.getcwd()) == "notebooks":
    os.chdir("..")
dj.config.load("dj_local_conf.json")  # load config for database connection info

import spyglass.common as sgc
import spyglass.spikesorting as sgs
import spyglass.decoding as sgd
import spyglass.utils as sgu

# ignore datajoint+jupyter async warnings
import warnings

warnings.simplefilter("ignore", category=DeprecationWarning)
warnings.simplefilter("ignore", category=ResourceWarning)
warnings.simplefilter("ignore", category=UserWarning)

import os import datajoint as dj from pprint import pprint # change to the upper level folder to detect dj_local_conf.json if os.path.basename(os.getcwd()) == "notebooks": os.chdir("..") dj.config.load("dj_local_conf.json") # load config for database connection info import spyglass.common as sgc import spyglass.spikesorting as sgs import spyglass.decoding as sgd import spyglass.utils as sgu # ignore datajoint+jupyter async warnings import warnings warnings.simplefilter("ignore", category=DeprecationWarning) warnings.simplefilter("ignore", category=ResourceWarning) warnings.simplefilter("ignore", category=UserWarning)

[2023-08-02 18:58:19,324][INFO]: Connecting root@localhost:3306
[2023-08-02 18:58:19,360][INFO]: Connected root@localhost:3306
/home/cb/miniconda3/envs/spy/lib/python3.9/site-packages/spikeinterface/sortingcomponents/peak_detection.py:643: NumbaDeprecationWarning: The 'nopython' keyword argument was not supplied to the 'numba.jit' decorator. The implicit default value for this argument is currently False, but it will be changed to True in Numba 0.59.0. See https://numba.readthedocs.io/en/stable/reference/deprecation.html#deprecation-of-object-mode-fall-back-behaviour-when-using-jit for details.
  @numba.jit(parallel=False)
/home/cb/miniconda3/envs/spy/lib/python3.9/site-packages/spikeinterface/sortingcomponents/peak_detection.py:668: NumbaDeprecationWarning: The 'nopython' keyword argument was not supplied to the 'numba.jit' decorator. The implicit default value for this argument is currently False, but it will be changed to True in Numba 0.59.0. See https://numba.readthedocs.io/en/stable/reference/deprecation.html#deprecation-of-object-mode-fall-back-behaviour-when-using-jit for details.
  @numba.jit(parallel=False)
Cupy is not installed or GPU is not detected. Ignore this message if not using GPU

Select Data¶

In [ ]:

nwb_file_name = "J1620210531.nwb"
nwb_copy_file_name = sgu.nwb_helper_fn.get_nwb_copy_filename(nwb_file_name)

nwb_file_name = "J1620210531.nwb" nwb_copy_file_name = sgu.nwb_helper_fn.get_nwb_copy_filename(nwb_file_name)

Spike Sorting Selection¶

We can investigate the populate_mark_indicators function we want to use with ?:

In [2]:

?sgd.clusterless.populate_mark_indicators

?sgd.clusterless.populate_mark_indicators

/home/edeno/miniconda3/envs/spyglass/lib/python3.8/site-packages/seaborn/rcmod.py:82: DeprecationWarning: distutils Version classes are deprecated. Use packaging.version instead.
  if LooseVersion(mpl.__version__) >= "3.0":
/home/edeno/miniconda3/envs/spyglass/lib/python3.8/site-packages/setuptools/_distutils/version.py:346: DeprecationWarning: distutils Version classes are deprecated. Use packaging.version instead.
  other = LooseVersion(other)

Connecting edeno@lmf-db.cin.ucsf.edu:3306

Signature:
populate_mark_indicators(
    spikesorting_selection_keys: list,
    mark_param_name='default',
    position_info_param_name='default_decoding',
)
Docstring: <no docstring>
File:      /stelmo/edeno/nwb_datajoint/src/spyglass/decoding/clusterless.py
Type:      function

From the docstring, we see that we need to supply spikesorting_selection_keys, which is a list of SpikeSorting keys as dictionaries. We provide a list to extract marks for many electrode at once.

Here are the primary keys required by SpikeSorting:

In [3]:

sgs.SpikeSorting.primary_key

sgs.SpikeSorting.primary_key

Out[3]:

['nwb_file_name',
 'sort_group_id',
 'sort_interval_name',
 'preproc_params_name',
 'team_name',
 'sorter',
 'sorter_params_name',
 'artifact_removed_interval_list_name']

Here is an example of what spikesorting_selection_keys should look like:

In [4]:

spikesorting_selections = [
    {
        "nwb_file_name": "J1620210531_.nwb",
        "sort_group_id": 0,
        "sort_interval_name": "raw data valid times no premaze no home",
        "preproc_params_name": "franklab_tetrode_hippocampus",
        "team_name": "JG_DG",
        "sorter": "clusterless_thresholder",
        "sorter_params_name": "clusterless_fixed",
        "artifact_removed_interval_list_name": "J1620210531_.nwb_0_raw data valid times no premaze no home_franklab_tetrode_hippocampus_JG_DG_group_0.8_2000_8_1_artifact_removed_valid_times",
    },
    {
        "nwb_file_name": "J1620210531_.nwb",
        "sort_group_id": 1,
        "sort_interval_name": "raw data valid times no premaze no home",
        "preproc_params_name": "franklab_tetrode_hippocampus",
        "team_name": "JG_DG",
        "sorter": "clusterless_thresholder",
        "sorter_params_name": "clusterless_fixed",
        "artifact_removed_interval_list_name": "J1620210531_.nwb_1_raw data valid times no premaze no home_franklab_tetrode_hippocampus_JG_DG_group_0.8_2000_8_1_artifact_removed_valid_times",
    },
]

spikesorting_selections = [ { "nwb_file_name": "J1620210531_.nwb", "sort_group_id": 0, "sort_interval_name": "raw data valid times no premaze no home", "preproc_params_name": "franklab_tetrode_hippocampus", "team_name": "JG_DG", "sorter": "clusterless_thresholder", "sorter_params_name": "clusterless_fixed", "artifact_removed_interval_list_name": "J1620210531_.nwb_0_raw data valid times no premaze no home_franklab_tetrode_hippocampus_JG_DG_group_0.8_2000_8_1_artifact_removed_valid_times", }, { "nwb_file_name": "J1620210531_.nwb", "sort_group_id": 1, "sort_interval_name": "raw data valid times no premaze no home", "preproc_params_name": "franklab_tetrode_hippocampus", "team_name": "JG_DG", "sorter": "clusterless_thresholder", "sorter_params_name": "clusterless_fixed", "artifact_removed_interval_list_name": "J1620210531_.nwb_1_raw data valid times no premaze no home_franklab_tetrode_hippocampus_JG_DG_group_0.8_2000_8_1_artifact_removed_valid_times", }, ]

WARNING: This process relies on both SpikeSortingSelection and IntervalPositionInfo. You can check your database with the following:

In [5]:

sgs.SpikeSortingSelection & {
    "nwb_file_name": nwb_copy_file_name,
    "sorter": "clusterless_thresholder",
}

sgs.SpikeSortingSelection & { "nwb_file_name": nwb_copy_file_name, "sorter": "clusterless_thresholder", }

Out[5]:

Table for holding selection of recording and parameters for each spike sorting run

nwb_file_name name of the NWB file	sort_group_id identifier for a group of electrodes	sort_interval_name name for this interval	preproc_params_name	team_name	sorter	sorter_params_name	artifact_removed_interval_list_name	import_path optional path to previous curated sorting output
J1620210531_.nwb	0	raw data valid times no premaze no home	franklab_tetrode_hippocampus	JG_DG	clusterless_thresholder	clusterless_fixed	J1620210531_.nwb_0_raw data valid times no premaze no home_franklab_tetrode_hippocampus_JG_DG_group_0.8_2000_8_1_artifact_removed_valid_times
J1620210531_.nwb	1	raw data valid times no premaze no home	franklab_tetrode_hippocampus	JG_DG	clusterless_thresholder	clusterless_fixed	J1620210531_.nwb_1_raw data valid times no premaze no home_franklab_tetrode_hippocampus_JG_DG_group_0.8_2000_8_1_artifact_removed_valid_times

Total: 2

Remember to replace the nwb_file_name with your own nwb file name

In [6]:

sgc.IntervalPositionInfo & {
    "nwb_file_name": nwb_copy_file_name,
    "position_info_param_name": "default_decoding",
}

sgc.IntervalPositionInfo & { "nwb_file_name": nwb_copy_file_name, "position_info_param_name": "default_decoding", }

Out[6]:

position_info_param_name name for this set of parameters	nwb_file_name name of the NWB file	interval_list_name descriptive name of this interval list	analysis_file_name name of the file	head_position_object_id	head_orientation_object_id	head_velocity_object_id
default_decoding	J1620210531_.nwb	pos 0 valid times	J1620210531_XOKZ4G53LE.nwb	200c54a9-57dc-4ed4-8e48-85cc9387b922	26f4ec71-2f5f-4ef2-82f1-ee3a89048168	7f9ffe2f-31e5-47ac-bad9-558440cd5f1d
default_decoding	J1620210531_.nwb	pos 1 valid times	J1620210531_B257TS35XJ.nwb	c164cdfd-c328-49b2-b6b4-f8663a17f5d6	e1f26617-c44b-4464-942d-5e6755606789	f3c69ac6-5f0a-4622-827b-44a769ce4f77
default_decoding	J1620210531_.nwb	pos 10 valid times	J1620210531_1RICS57YYG.nwb	2394ec2d-c076-4b11-a40b-91e4b51b81fa	005080ee-b93f-4b3c-bcd0-595b46fedab2	47158c4f-3632-4ce1-9f1e-043f1ea5a5ec
default_decoding	J1620210531_.nwb	pos 11 valid times	J1620210531_HZSI1BGTE7.nwb	295aa187-81cd-4209-b96d-163f12a716ed	19a95d85-8cb9-4e06-82ee-26bc6df7c8c8	9abb39ce-f0e2-4637-b9e9-5f7b6891201d
default_decoding	J1620210531_.nwb	pos 12 valid times	J1620210531_M0UMC7TZ49.nwb	ae4c015c-9d5f-4ca3-bbcd-049c1e1ae50b	1bbc244c-4d85-4e63-9d69-10815e37276c	be8f6ba5-3cc9-4ac3-96af-5c3e84b9851c
default_decoding	J1620210531_.nwb	pos 13 valid times	J1620210531_TREA1SOT03.nwb	c4bfe562-f523-495c-99e5-681524661264	65023b5e-321b-4d6e-b975-ec03cbd46247	ce391291-6107-420a-a73d-9a18d343a47a
default_decoding	J1620210531_.nwb	pos 14 valid times	J1620210531_93PXV7F4CX.nwb	8521f813-635d-41cd-8f10-b00f26a7f957	75d08124-57f7-481b-ae44-0752a5912c18	84ce4054-8c11-49f2-8295-7d8173cb7eb7
default_decoding	J1620210531_.nwb	pos 15 valid times	J1620210531_FUJ1B1OKOA.nwb	e72e5fcd-3d80-463a-a9d1-305e6e99f8db	6e8dc0e2-ab02-4305-bf2e-e2d208bae77a	b7589719-49ba-48e7-a20b-88a911c3f8a5
default_decoding	J1620210531_.nwb	pos 16 valid times	J1620210531_76HL0SNP59.nwb	b3124712-55db-4b0b-9bdc-77f03a0b8c98	08bfd82c-1e7c-4aa7-bd9e-256577b26e9c	85ffd435-3dc4-49ab-adea-a3327817a51d
default_decoding	J1620210531_.nwb	pos 17 valid times	J1620210531_5F8VRT0OC4.nwb	78b8f7e4-fb90-438d-8551-d15a4d97484a	7b96d15c-9721-44ea-9ecd-d8a2b60711d6	12336768-227e-4c0c-9ade-b3c7ac6c2de3
default_decoding	J1620210531_.nwb	pos 18 valid times	J1620210531_PP7XK1G26S.nwb	39ab3238-a0dd-4906-95c5-8e534458e371	ca0f939d-f0a7-4df4-9946-2f605ef5ab76	571277d0-885e-4d0e-b802-6ad8047ee3f1
default_decoding	J1620210531_.nwb	pos 19 valid times	J1620210531_AQZHSP2IDM.nwb	b5a21053-3239-4943-aee1-3227b4915d5f	9f0c61c8-90ed-4a74-873c-e250aa872a2a	714f6ceb-6d70-474c-8c42-6f9fa3ff77e1

...

Total: 21

Populate Mark Indicators¶

Now that we've checked those, we can run the function:

In [7]:

sgd.clusterless.populate_mark_indicators(spikesorting_selections)

sgd.clusterless.populate_mark_indicators(spikesorting_selections)

We can verify that this worked:

In [8]:

plt.plot(position_info.head_position_x, position_info.head_position_y)

plt.plot(position_info.head_position_x, position_info.head_position_y)

Out[8]:

curation_id a number correponding to the index of this curation	nwb_file_name name of the NWB file	sort_group_id identifier for a group of electrodes	sort_interval_name name for this interval	preproc_params_name	team_name	sorter	sorter_params_name	artifact_removed_interval_list_name	mark_param_name a name for this set of parameters	interval_list_name descriptive name of this interval list	sampling_rate	analysis_file_name name of the file	marks_indicator_object_id
0	J1620210531_.nwb	0	raw data valid times no premaze no home	franklab_tetrode_hippocampus	JG_DG	clusterless_thresholder	clusterless_fixed	J1620210531_.nwb_0_raw data valid times no premaze no home_franklab_tetrode_hippocampus_JG_DG_group_0.8_2000_8_1_artifact_removed_valid_times	default	pos 0 valid times	500.0	J1620210531_NS19JB65RU.nwb	d0de79cc-f2be-4e86-b4e8-e58a8aef1c02
0	J1620210531_.nwb	0	raw data valid times no premaze no home	franklab_tetrode_hippocampus	JG_DG	clusterless_thresholder	clusterless_fixed	J1620210531_.nwb_0_raw data valid times no premaze no home_franklab_tetrode_hippocampus_JG_DG_group_0.8_2000_8_1_artifact_removed_valid_times	default	pos 1 valid times	500.0	J1620210531_YP9MXAPE2G.nwb	7598dc69-a024-4140-8b37-d1342a0e7829
0	J1620210531_.nwb	0	raw data valid times no premaze no home	franklab_tetrode_hippocampus	JG_DG	clusterless_thresholder	clusterless_fixed	J1620210531_.nwb_0_raw data valid times no premaze no home_franklab_tetrode_hippocampus_JG_DG_group_0.8_2000_8_1_artifact_removed_valid_times	default	pos 10 valid times	500.0	J1620210531_AGOI7DXRQX.nwb	12a60beb-3ac2-41ae-a091-eebd0b14672c
0	J1620210531_.nwb	0	raw data valid times no premaze no home	franklab_tetrode_hippocampus	JG_DG	clusterless_thresholder	clusterless_fixed	J1620210531_.nwb_0_raw data valid times no premaze no home_franklab_tetrode_hippocampus_JG_DG_group_0.8_2000_8_1_artifact_removed_valid_times	default	pos 11 valid times	500.0	J1620210531_VXGQCG8UYO.nwb	d0d40c96-e5a1-494f-b8a7-bf475304d640
0	J1620210531_.nwb	0	raw data valid times no premaze no home	franklab_tetrode_hippocampus	JG_DG	clusterless_thresholder	clusterless_fixed	J1620210531_.nwb_0_raw data valid times no premaze no home_franklab_tetrode_hippocampus_JG_DG_group_0.8_2000_8_1_artifact_removed_valid_times	default	pos 12 valid times	500.0	J1620210531_NF8RWZGCSG.nwb	2b6476a7-143f-4760-bafa-e4959f0e735a
0	J1620210531_.nwb	0	raw data valid times no premaze no home	franklab_tetrode_hippocampus	JG_DG	clusterless_thresholder	clusterless_fixed	J1620210531_.nwb_0_raw data valid times no premaze no home_franklab_tetrode_hippocampus_JG_DG_group_0.8_2000_8_1_artifact_removed_valid_times	default	pos 13 valid times	500.0	J1620210531_F7OHK7UMO3.nwb	84aea599-dbf1-4d11-bca2-00aa6ae9967a
0	J1620210531_.nwb	0	raw data valid times no premaze no home	franklab_tetrode_hippocampus	JG_DG	clusterless_thresholder	clusterless_fixed	J1620210531_.nwb_0_raw data valid times no premaze no home_franklab_tetrode_hippocampus_JG_DG_group_0.8_2000_8_1_artifact_removed_valid_times	default	pos 14 valid times	500.0	J1620210531_MPXHIH43Y1.nwb	78224e49-0118-458d-9aee-727db98fc306
0	J1620210531_.nwb	0	raw data valid times no premaze no home	franklab_tetrode_hippocampus	JG_DG	clusterless_thresholder	clusterless_fixed	J1620210531_.nwb_0_raw data valid times no premaze no home_franklab_tetrode_hippocampus_JG_DG_group_0.8_2000_8_1_artifact_removed_valid_times	default	pos 15 valid times	500.0	J1620210531_YFG1894358.nwb	721526f8-df6d-442e-84ee-bf7dd6ff8c19
0	J1620210531_.nwb	0	raw data valid times no premaze no home	franklab_tetrode_hippocampus	JG_DG	clusterless_thresholder	clusterless_fixed	J1620210531_.nwb_0_raw data valid times no premaze no home_franklab_tetrode_hippocampus_JG_DG_group_0.8_2000_8_1_artifact_removed_valid_times	default	pos 16 valid times	500.0	J1620210531_VJ5V0LRJKU.nwb	39258f97-4508-4068-9b2b-ce419a5d718d
0	J1620210531_.nwb	0	raw data valid times no premaze no home	franklab_tetrode_hippocampus	JG_DG	clusterless_thresholder	clusterless_fixed	J1620210531_.nwb_0_raw data valid times no premaze no home_franklab_tetrode_hippocampus_JG_DG_group_0.8_2000_8_1_artifact_removed_valid_times	default	pos 17 valid times	500.0	J1620210531_NACXLH30KV.nwb	bf312f88-af31-4247-83dc-9633d191401a
0	J1620210531_.nwb	0	raw data valid times no premaze no home	franklab_tetrode_hippocampus	JG_DG	clusterless_thresholder	clusterless_fixed	J1620210531_.nwb_0_raw data valid times no premaze no home_franklab_tetrode_hippocampus_JG_DG_group_0.8_2000_8_1_artifact_removed_valid_times	default	pos 18 valid times	500.0	J1620210531_LFKSWYVVRS.nwb	af89e3e1-71f7-4c7c-a5ef-94d3cc7a1203
0	J1620210531_.nwb	0	raw data valid times no premaze no home	franklab_tetrode_hippocampus	JG_DG	clusterless_thresholder	clusterless_fixed	J1620210531_.nwb_0_raw data valid times no premaze no home_franklab_tetrode_hippocampus_JG_DG_group_0.8_2000_8_1_artifact_removed_valid_times	default	pos 19 valid times	500.0	J1620210531_R9V8JD4IQA.nwb	3616a7b7-fca2-43bb-9c72-44eb3c22af80

...

Total: 42

In [9]:

position_info.shape, marks.shape

position_info.shape, marks.shape

Out[9]:

((655645, 6), (655645, 4, 22))

In [10]:

from spyglass.common.common_interval import interval_list_intersect
from spyglass.common import IntervalList

key = {}
key["interval_list_name"] = "02_r1"
key["nwb_file_name"] = nwb_copy_file_name

interval = (
    IntervalList
    & {
        "nwb_file_name": key["nwb_file_name"],
        "interval_list_name": key["interval_list_name"],
    }
).fetch1("valid_times")

valid_ephys_times = (
    IntervalList
    & {
        "nwb_file_name": key["nwb_file_name"],
        "interval_list_name": "raw data valid times",
    }
).fetch1("valid_times")
position_interval_names = (
    IntervalPositionInfo
    & {
        "nwb_file_name": key["nwb_file_name"],
        "position_info_param_name": "default_decoding",
    }
).fetch("interval_list_name")
valid_pos_times = [
    (
        IntervalList
        & {
            "nwb_file_name": key["nwb_file_name"],
            "interval_list_name": pos_interval_name,
        }
    ).fetch1("valid_times")
    for pos_interval_name in position_interval_names
]

intersect_interval = interval_list_intersect(
    interval_list_intersect(interval, valid_ephys_times), valid_pos_times[0]
)
valid_time_slice = slice(intersect_interval[0][0], intersect_interval[0][1])
valid_time_slice

from spyglass.common.common_interval import interval_list_intersect from spyglass.common import IntervalList key = {} key["interval_list_name"] = "02_r1" key["nwb_file_name"] = nwb_copy_file_name interval = ( IntervalList & { "nwb_file_name": key["nwb_file_name"], "interval_list_name": key["interval_list_name"], } ).fetch1("valid_times") valid_ephys_times = ( IntervalList & { "nwb_file_name": key["nwb_file_name"], "interval_list_name": "raw data valid times", } ).fetch1("valid_times") position_interval_names = ( IntervalPositionInfo & { "nwb_file_name": key["nwb_file_name"], "position_info_param_name": "default_decoding", } ).fetch("interval_list_name") valid_pos_times = [ ( IntervalList & { "nwb_file_name": key["nwb_file_name"], "interval_list_name": pos_interval_name, } ).fetch1("valid_times") for pos_interval_name in position_interval_names ] intersect_interval = interval_list_intersect( interval_list_intersect(interval, valid_ephys_times), valid_pos_times[0] ) valid_time_slice = slice(intersect_interval[0][0], intersect_interval[0][1]) valid_time_slice

Out[10]:

slice(1581886916.3153033, 1581888227.5987928, None)

In [ ]:

from replay_trajectory_classification import ClusterlessClassifier
from replay_trajectory_classification.environments import Environment
from replay_trajectory_classification.continuous_state_transitions import (
    RandomWalk,
    Uniform,
)
from spyglass.decoding.clusterless import ClusterlessClassifierParameters

marks = marks.sel(time=valid_time_slice)
position_info = position_info.loc[valid_time_slice]

parameters = (
    ClusterlessClassifierParameters()
    & {"classifier_param_name": "default_decoding_gpu"}
).fetch1()

parameters["classifier_params"]["clusterless_algorithm_params"] = {
    "mark_std": 24.0,
    "position_std": 3.0,
    "block_size": int(2**13),
    "disable_progress_bar": False,
    "use_diffusion": False,
}
parameters["classifier_params"]["environments"][0] = Environment(
    place_bin_size=3.0
)


import cupy as cp

with cp.cuda.Device(0):
    classifier = ClusterlessClassifier(**parameters["classifier_params"])
    classifier.fit(
        position=position_info[["head_position_x", "head_position_y"]].values,
        multiunits=marks.values,
        **parameters["fit_params"],
    )
    results = classifier.predict(
        multiunits=marks.values,
        time=position_info.index,
        **parameters["predict_params"],
    )
    logging.info("Done!")

from replay_trajectory_classification import ClusterlessClassifier from replay_trajectory_classification.environments import Environment from replay_trajectory_classification.continuous_state_transitions import ( RandomWalk, Uniform, ) from spyglass.decoding.clusterless import ClusterlessClassifierParameters marks = marks.sel(time=valid_time_slice) position_info = position_info.loc[valid_time_slice] parameters = ( ClusterlessClassifierParameters() & {"classifier_param_name": "default_decoding_gpu"} ).fetch1() parameters["classifier_params"]["clusterless_algorithm_params"] = { "mark_std": 24.0, "position_std": 3.0, "block_size": int(2**13), "disable_progress_bar": False, "use_diffusion": False, } parameters["classifier_params"]["environments"][0] = Environment( place_bin_size=3.0 ) import cupy as cp with cp.cuda.Device(0): classifier = ClusterlessClassifier(**parameters["classifier_params"]) classifier.fit( position=position_info[["head_position_x", "head_position_y"]].values, multiunits=marks.values, **parameters["fit_params"], ) results = classifier.predict( multiunits=marks.values, time=position_info.index, **parameters["predict_params"], ) logging.info("Done!")

In [16]:

from spyglass.decoding.visualization import (
    create_interactive_2D_decoding_figurl,
)


view = create_interactive_2D_decoding_figurl(
    position_info,
    marks,
    results,
    classifier.environments[0].place_bin_size,
    position_name=["head_position_x", "head_position_y"],
    head_direction_name="head_orientation",
    speed_name="head_speed",
    posterior_type="acausal_posterior",
    sampling_frequency=500,
    view_height=800,
)

from spyglass.decoding.visualization import ( create_interactive_2D_decoding_figurl, ) view = create_interactive_2D_decoding_figurl( position_info, marks, results, classifier.environments[0].place_bin_size, position_name=["head_position_x", "head_position_y"], head_direction_name="head_orientation", speed_name="head_speed", posterior_type="acausal_posterior", sampling_frequency=500, view_height=800, )

Up Next¶

Next, we'll start the process of decoding representations of position with ephys data. This can be done either with GPUs or clusterless.